Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Claims 1-22 are pending.
Examiner Notes
Examiner cites particular paragraphs and/or columns and lines in the references as applied to Applicant’s claims for the convenience of the Applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the Applicant fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. The prompt development of a clear issue requires that the replies of the Applicant meet the objections to and rejections of the claims. Applicant should also specifically point out the support for any amendments made to the disclosure. See MPEP § 2163.06.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Authorization for Internet Communications in a Patent Application
Applicant is encouraged to file an Authorization for Internet Communications in a Patent Application form (http://www.uspto.gov/sites/default/files/documents/sb0439.pdf) along with the response to this office action to facilitate and expedite future communication between Applicant and the examiner. If the form is submitted then Applicant is requested to provide a contact email address in the signature block at the conclusion of the official reply.
Allowable Subject Matter
Claims 8-9 and 18-19 are objected to as being dependent upon a rejected base claim, but would be allowable over the prior art of record if rewritten to overcome the applicable rejection(s) and/or objection(s) set forth in this Office action and to include all of the limitations of the base claim and any intervening claims because the examiner found neither prior art cited in its entirety, nor based on the prior art, found any motivation to combine any of the said prior art.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (an abstract idea) without significantly more.
Step 1: The claim is a process, machine, manufacture, or composition of matter:
Claim 1. A computer-implemented method comprising.
Step 2A Prong One: The claim recites an abstract idea because it includes limitations that can be considered mental processes (concepts performed in the human mind including an observation, evaluation, judgment, and/or opinion). If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the human mind or via pen and paper, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea:
determining, by the computing device, a resultant scaling factor based on the one or more fields selected by the associated query and the data shape of those one or more fields in the data object, at least one of the one or more fields having an associated scale factor specified in the schema (abstract idea mental process);
adjusting an allocation limit based on the resultant scaling factor to produce an adjusted allocation limit, the allocation limit being a per-job allocation of a computing resource for execution of the job request (abstract idea mental process).
Step 2A Prong Two: The abstract idea is not integrated into a practical application because the abstract idea is recited but for generically recited additional computer elements (i.e. data storage, processor, memory, computer readable medium, etc.) which do not add meaningful limitations to the abstract idea amounting to simply implementing the abstract idea on a generic computer using generic computing hardware and/or software (e.g. generally linking the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The generic computing components are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using the recited generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea:
receiving, at a computing device (generic computing components), a job request having an associated query referencing one or more fields of a data object conforming to a schema (generic computing components performing extra-solution activity of receiving data/information);
executing, by the computing device, the job request subject to the adjusted allocation limit (generic computing components performing extra-solution activity of merely reciting the words "apply it" or an equivalent with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using the computer as a tool to perform the abstract idea).
Step 2B: The claim includes limitations which can be considered extra-solution activity (see MPEP 2106.05(g)) insufficient to amount to significantly more than the abstract idea because the additional limitations only perform at least one of collecting, gathering, displaying, generating, modifying, updating, storing, retrieving, sending, and receiving data/information data which are well-understood, routine, conventional computer functions as recognized by the court decisions listed in MPEP § 2106.05(d)II. The claim further includes limitations that do not integrate the judicial exception into a practical application because they merely recite the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). Therefore, the claim, and its limitations when considered separately and in combination, is directed to patent ineligible subject matter:
receiving, at a computing device, a job request having an associated query referencing one or more fields of a data object conforming to a schema (extra-solution activity of receiving data/information);
executing, by the computing device, the job request subject to the adjusted allocation limit (extra-solution activity of merely reciting the words "apply it" or an equivalent with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using the computer as a tool to perform the abstract idea).
Claim 2. The method of claim 1, wherein receiving includes receiving a compute job to execute (extra-solution activity of receiving data/information), wherein the query is associated with the compute job, and wherein executing includes executing the compute job subject to the adjusted allocation limit (extra-solution activity of merely reciting the words "apply it" or an equivalent with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using the computer as a tool to perform the abstract idea).
Claim 3. The method of claim 1, wherein executing the job request subject to the adjusted allocation limited includes:
comparing a job execution parameter to the adjusted allocation limit (abstract idea mental process);
determining that the job execution parameter exceeds the adjusted allocation limit (abstract idea mental process); and
responsive to determining that the job execution parameter exceeds the adjusted allocation limit, terminating execution of the job request prior to its completion (extra-solution activity of merely reciting the words "apply it" or an equivalent with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using the computer as a tool to perform the abstract idea).
Claim 4. The method of claim 3, wherein the job execution parameter corresponds to a least one of a CPU instruction count, a virtual machine instruction count, or processor time (abstract idea mental process).
Claim 5. The method of claim 1, wherein the data shape includes a count of the one or more fields in the data object (abstract idea mental process).
Claim 6. The method of claim 1, wherein the data shape includes a size of the one or more fields in the data object (abstract idea mental process).
Claim 7. The method of claim 1, wherein determining the resultant scaling factor includes multiplying the associated scale factor for one of said at least one of the one or more fields of the schema by a field size or row count for the one of said one or more fields in the data object (abstract idea mental process).
Claim 8. The method of claim 1, wherein the schema specifies a first scale factor associated with a first one of the one or more fields and a second scale factor associated with a second one of the one or more fields (abstract idea mental process), and wherein determining the resultant scaling factor includes:
determining a first resultant scaling factor based on the first scale factor and the data shape of the first one of the one or more fields in the data object (abstract idea mental process);
determining a second resultant scaling factor based on the second scale factor and the data shape of the second one of the one or more fields in the data object (abstract idea mental process); and
determining the resultant scaling factor using the first resultant scaling factor and the second resultant scaling factor (abstract idea mental process).
Claim 9. The method of claim 8, wherein determining the resultant scaling factor using the first resultant scaling factor and the second resultant scaling factor includes comparing and selecting the larger of the first resultant scaling factor and the second resultant scaling factor as the resultant scaling factor (abstract idea mental process).
Claim 10. The method of claim 1, wherein the associated query is a GraphQL query and wherein the schema includes a directive specifying the associated scale factor associated with the at least one of the one or more fields (extra-solution activity of receiving data/information).
Claim 11. The method of claim 1, wherein the computing device comprises a multi-user computing platform and the job request includes an application program executing on the multi-user computing platform (generic computing components) in response to a customer user input received at the multi-user computing platform (extra-solution activity of receiving data/information), and wherein the data object is a user-specific data object generated in response to customer user input activity on the multi-user computing platform (extra-solution activity of receiving data/information).
As per claim 12, it has similar limitations as claim 1 and is therefore rejected using the same rationale.
As per claim 13, it has similar limitations as claim 3 and is therefore rejected using the same rationale.
As per claim 14, it has similar limitations as claim 4 and is therefore rejected using the same rationale.
As per claim 15, it has similar limitations as claim 5 and is therefore rejected using the same rationale.
As per claim 16, it has similar limitations as claim 6 and is therefore rejected using the same rationale.
As per claim 7, it has similar limitations as claim 7 and is therefore rejected using the same rationale.
As per claim 18, it has similar limitations as claim 8 and is therefore rejected using the same rationale.
As per claim 19, it has similar limitations as claim 9 and is therefore rejected using the same rationale.
As per claim 20, it has similar limitations as claim 10 and is therefore rejected using the same rationale.
As per claim 21, it has similar limitations as claim 11 and is therefore rejected using the same rationale.
As per claim 22, it has similar limitations as claim 1 and is therefore rejected using the same rationale.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 5, 10, 12, 15, 20, and 22 are rejected under 35 U.S.C. 103 as being unpatentable over
Stephen et al. (US 2024/0036910) (hereinafter Stephen) in view of
McGreevy (US 2003/0078913) in view of
Rajan et al. (US 10,108,648) (hereinafter Rajan)
Katroulis (US 2022/0067046) in view of
Knaup et al. (US 2019/0171495) (hereinafter Knaup).
As per claim 1, Stephen primarily teaches the invention as claimed including a computer-implemented method comprising:
receiving, at a computing device, a job request having an associated query referencing one or more fields of a data object conforming to a schema ([0101] example schema and built-in directives can control query-execution to omit or include certain fields in returned data objects based on variables evaluated at the query-execution time and [0103] example schema specifies three different types of queries that request information that can be directed, by a client, to the server via the GraphQL interface associated with data objects).
Stephen does not explicitly teach:
determining, by the computing device, a resultant scaling factor based on the one or more fields selected by the associated query and the data shape of those one or more fields in the data object, at least one of the one or more fields having an associated scale factor specified in the schema;
adjusting an allocation limit based on the resultant scaling factor to produce an adjusted allocation limit, the allocation limit being a per-job allocation of a computing resource for execution of the job request; and
executing, by the computing device, the job request subject to the adjusted allocation limit.
However, McGreevy teaches:
determining, by the computing device, a resultant scaling factor based on the one or more fields selected by the associated query ([0199]-[0200] the query includes a number of query fields and inputting the query can also include assigning a weight to at least one of the query fields. Each one of the relational summation metrics corresponding to the selected query field is scaled by a factor determined by the assigned weight. This allows each query field to be given an importance value relative to the other query fields).
McGreevy and Stephen are both concerned with query management in computing environments and are therefore combinable/modifiable. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Stephen in view of McGreevy because it would provide a way of searching a database for subsets of the database that are relevant to an input query by first providing a number of relational models of subsets of a database are provided, inputting a query including one or more key terms, and creating a gleaning model of the query. The gleaning model of the query is then compared to each one of the relational models of subsets of the database, and the identifiers of the relevant subsets are then output.
Stephen in view of McGreevy do not explicitly teach:
determining, by the computing device, a resultant scaling factor based on the data shape of those one or more fields in the data object;
at least one of the one or more fields having an associated scale factor specified in the schema;
adjusting an allocation limit based on the resultant scaling factor to produce an adjusted allocation limit, the allocation limit being a per-job allocation of a computing resource for execution of the job request; and
executing, by the computing device, the job request subject to the adjusted allocation limit.
However, Rajan teaches:
determining, by the computing device, a resultant scaling factor based on the data shape of those one or more fields in the data object (col. 13, ll. 25-28 the duration or performance of the query can be scaled by the size of the dataset. In one example, a ratio or scaling factor can be developed based on the size of the dataset and fig. 3 illustrates a custom object represented as a custom field table including physical index columns).
Rajan and Stephen are both concerned with query management in computing environments and are therefore combinable/modifiable. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Stephen in view of McGreevy in view of Rajan because it would provide way of creating a custom index in a multi-tenant database environment by obtaining a query for a multi-tenant database that is recommended as a candidate for creating an additional filter, evaluating the query against criteria to determine whether to select the query for creating the additional filter, and creating the additional filter for the query, if the query is selected. This can provide more reliable and faster execution of queries both in development and in production.
Stephen in view of McGreevy in view of Rajan do not explicitly teach:
at least one of the one or more fields having an associated scale factor specified in the schema;
adjusting an allocation limit based on the resultant scaling factor to produce an adjusted allocation limit, the allocation limit being a per-job allocation of a computing resource for execution of the job request; and
executing, by the computing device, the job request subject to the adjusted allocation limit.
However, Katroulis teaches:
at least one of the one or more fields having an associated scale factor specified in the schema ([0023] each field of the schema may be associated with hundreds or thousands of records as indicated by scale factor).
Katroulis and Stephen are both concerned with query management in computing environments and are therefore combinable/modifiable. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Stephen in view of McGreevy in view of Rajan in view of Katroulis because it would provide for access path selection optimization for relational database queries. Machine learning systems, such as neural networks, may be used to determine costs for each path. A neural network may be trained based on cost determinations from scans and indexes of the tree for an initial set of queries, and then may be used to predict costs for additional queries such that a path may be selected. The training data may be periodically refreshed or updated, or may be refreshed or updated responsive to changes in hardware or computing environment.
Stephen in view of McGreevy in view of Rajan in view of Katroulis do not explicitly teach:
adjusting an allocation limit based on the resultant scaling factor to produce an adjusted allocation limit, the allocation limit being a per-job allocation of a computing resource for execution of the job request; and
executing, by the computing device, the job request subject to the adjusted allocation limit.
However, Knaup teaches:
adjusting an allocation limit based on the resultant scaling factor to produce an adjusted allocation limit ([0019] resize resources allocated to a task based on multiplying a required resource size by a scaling factor), the allocation limit being a per-job allocation of a computing resource for execution of the job request ([0018] and [0020] resources for each task and required resource sizes for each task); and
executing, by the computing device, the job request subject to the adjusted allocation limit ([0028] receiving an indication of a job to run, indicating to a worker system to execute a task, for receiving task resource usage data from a worker system, for providing task resource usage data to storage system, for determining a required resource size to run a task from task resource data stored in storage system, and for resizing resources allocated to a task to a required resource size).
Knaup and Stephen are both concerned with managing job/task requests in computing environments and are therefore combinable/modifiable. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Stephen in view of McGreevy in view of Rajan in view of Katroulis in view of Knaup because it would provide a way for efficiently using cluster computing resources by executing a process for efficiently using the worker systems e.g., reducing the number of worker systems used to run the set of tasks by using a multistep automated scaling process for cluster containers e.g., determining required resources based on history, resizing based in determined resources, arranging tasks on workers, and deallocating unused workers.
As per claim 2, Knaup teaches wherein receiving includes receiving a compute job to execute ([0028] receiving an indication of a job to run), wherein the query is associated with the compute job ([0026] querying the status of jobs), and wherein executing includes executing the compute job subject to the adjusted allocation limit ([0028] receiving an indication of a job to run, indicating to a worker system to execute a task, for receiving task resource usage data from a worker system, for providing task resource usage data to storage system, for determining a required resource size to run a task from task resource data stored in storage system, and for resizing resources allocated to a task to a required resource size).
As per claim 5, McGeevy teaches wherein the data shape includes a count of the one or more fields in the data object ([0199] the query includes a number of query fields).
As per claim 10, the combination of references above teaches the method of claim 1, wherein the associated query is a GraphQL query (Stephen [0128] GraphQL query) and wherein the schema includes a directive specifying the associated scale factor associated with the at least one of the one or more fields (Katroulis [0023] each field of the schema may be associated with hundreds or thousands of records as indicated by scale factor).
As per claim 12, it has similar limitations as claim 1 and is therefore rejected using the same rationale.
As per claim 15, it has similar limitations as claim 5 and is therefore rejected using the same rationale.
As per claim 20, it has similar limitations as claim 10 and is therefore rejected using the same rationale.
As per claim 22, it has similar limitations as claim 1 and is therefore rejected using the same rationale.
Claims 3-4 and 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over Stephen in view of McGreevy in view of Rajan in view of Katroulis in view of Knaup in view of Barsness et al. (US 2012/0054514) (hereinafter Barsness).
As per claim 3, Stephen in view of McGreevy in view of Rajan in view of Katroulis in view of Knaup do not explicitly teach wherein executing the job request subject to the adjusted allocation limited includes: comparing a job execution parameter to the adjusted allocation limit; determining that the job execution parameter exceeds the adjusted allocation limit; and responsive to determining that the job execution parameter exceeds the adjusted allocation limit, terminating execution of the job request prior to its completion.
However, Barsness teaches wherein executing the job request subject to the adjusted allocation limited includes: comparing a job execution parameter to the adjusted allocation limit; determining that the job execution parameter exceeds the adjusted allocation limit; and responsive to determining that the job execution parameter exceeds the adjusted allocation limit, terminating execution of the job request prior to its completion ([0014] query governors generally control how long queries may execute. For example, a query governor may enable a database administrator to have queries time out i.e., execution of the query is halted if a predetermined amount of time elapses before the execution completes. Additionally, before the database executes the query, a query governor may estimate the time it will take to execute the query, and if the estimated time exceeds a threshold amount, may reject the query).
Barsness and Stephen are both concerned with query management in computing environments and are therefore combinable/modifiable. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Stephen in view of McGreevy in view of Rajan in view of Katroulis in view of Knaup in view of Barsness because it would provide for a query governor that may detect that a query will not be able to complete within the threshold amount of energy, even after the initial estimated energy consumption value is calculated, but before the threshold amount of energy has been consumed. As such, even when the initial estimated energy consumption value is incorrect, the resulting method can minimize any wasteful energy consumption by the query governor system.
As per claim 4, Barsness teaches wherein the job execution parameter corresponds to a least one of a CPU instruction count, a virtual machine instruction count, or processor time ([0014] query governors generally control how long queries may execute. For example, a query governor may enable a database administrator to have queries time out i.e., execution of the query is halted if a predetermined amount of time elapses before the execution completes. Additionally, before the database executes the query, a query governor may estimate the time it will take to execute the query, and if the estimated time exceeds a threshold amount, may reject the query).
As per claim 13, it has similar limitations as claim 3 and is therefore rejected using the same rationale.
As per claim 14, it has similar limitations as claim 4 and is therefore rejected using the same rationale.
Claims 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Stephen in view of McGreevy in view of Rajan in view of Katroulis in view of Knaup in view of Khan et al. (US 2022/0100750) (hereinafter Khan).
As per claim 6, Stephen in view of McGreevy in view of Rajan in view of Katroulis in view of Knaup do not explicitly teach wherein the data shape includes a size of the one or more fields in the data object.
However, Khan teaches wherein the data shape includes a size of the one or more fields in the data object ([0017] and [0022] data shape refers to properties of the data such as data type e.g., a format or filetype of the data, a list of data fields included in the data e.g., user ID, medication ID, administration events, etc., a size of the dataset e.g., a number of rows/columns).
Khan and Stephen are both concerned with query management in computing environments and are therefore combinable/modifiable. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Stephen in view of McGreevy in view of Rajan in view of Katroulis in view of Knaup in view of Khan because it would provide a way of submitting a response to a query at an operation including a confidence factor. This may be particularly advantageous to some query sources e.g., customers in that it can enable a query source to more properly utilize the data. For example, even if a customer has a relatively low confidence level requirement included in the query, the customer may benefit from knowing that data contained in a particular reply has an exceptionally high confidence rating.
As per claim 16, it has similar limitations as claim 6 and is therefore rejected using the same rationale.
Claims 7 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Stephen in view of McGreevy in view of Rajan in view of Katroulis in view of Knaup in view of Singh (US 2008/0114801).
As per claim 7, Stephen in view of McGreevy in view of Rajan in view of Katroulis in view of Knaup do not explicitly teach wherein determining the resultant scaling factor includes multiplying the associated scale factor for one of said at least one of the one or more fields of the schema by a field size or row count for the one of said one or more fields in the data object.
However, Singh teaches wherein determining the resultant scaling factor includes multiplying the associated scale factor for one of said at least one of the one or more fields of the schema by a field size or row count for the one of said one or more fields in the data object ([0069] scaling component can reduce the size of the statistically similar database by a user-selected scaling factor. The scaling factor can increase the size of the simulation database, simulating database growth. The resulting simulation database should be consistent with the statistics of the original database, but increased or reduced by the scaling factor. In particular, the table columns would remain the same, but the number of rows can be multiplied by the scaling factor).
Singh and Stephen are both concerned with query management in computing environments and are therefore combinable/modifiable. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Stephen in view of McGreevy in view of Rajan in view of Katroulis in view of Knaup in view of Singh because it would provide a way to facilitate database analysis, support and/or design by generating a simulation database, substantially statistically similar to the source database, but without copying the source data. This simulation database can be used to evaluate performance of the source database. Typically, databases maintain statistics describing the amount and distribution of data stored within the database. These statistics are used to optimize query response and are critical to analysis of database performance. The simulation database can be populated such that the statistics of the simulation database match the statistics of the source database without requiring copying of the actual source data.
As per claim 17, it has similar limitations as claim 7 and is therefore rejected using the same rationale.
Claims 11 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Stephen in view of McGreevy in view of Rajan in view of Katroulis in view of Knaup in view of Roberts et al. (US 2021/0027015) (hereinafter Roberts).
As per claim 11, Stephen in view of McGreevy in view of Rajan in view of Katroulis in view of Knaup do not explicitly teach wherein the computing device comprises a multi-user computing platform and the job request includes an application program executing on the multi-user computing platform in response to a customer user input received at the multi-user computing platform, and wherein the data object is a user-specific data object generated in response to customer user input activity on the multi-user computing platform.
However, Roberts teaches wherein the computing device comprises a multi-user computing platform and the job request includes an application program executing on the multi-user computing platform in response to a customer user input received at the multi-user computing platform, and wherein the data object is a user-specific data object generated in response to customer user input activity on the multi-user computing platform ([0032] multi-user platform for collaboration associated with document objects and interaction schemas).
Roberts and Stephen are both concerned with GraphQL in computing environments and are therefore combinable/modifiable. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Stephen in view of McGreevy in view of Rajan in view of Katroulis in view of Knaup in view of Roberts because it would enable use of templates to create electronic documents which thus allow a user with little to no programming skill to create an electronic document with complex functionalities, including, but not limited to, workflows and business processes. This may enable quick additions of API interactions through simple configuration and drag-and-drop tools.
As per claim 21, it has similar limitations as claim 11 and is therefore rejected using the same rationale.
Citation of Relevant Prior Art
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure:
Pilli et al. (US 2024/0394248) disclose data source introspection user interface for GraphQL API schema and resolver generation.
Pilli (US 2024/0411759) disclose GraphQL filter design for a GraphQL API schema.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Adam Lee whose telephone number is (571) 270-3369. The examiner can normally be reached on M-TH 8AM-5PM.
If attempts to reach the above noted Examiner by telephone are unsuccessful, the Examiner’s supervisor, Pierre Vital, can be reached at the following telephone number: (571) 272-4215. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Adam Lee/Primary Examiner, Art Unit 2198 August 25, 2026
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